DocumentCode
3151628
Title
Multi-sensor Information Fusion Based on Rough Set Theory
Author
Lv, Xiu-jiang ; Zhao, Yan ; Yao, Guang-shun ; Lv, Qiao-chu ; Wang, Ning
Author_Institution
Dept. of Electr. Eng., Changchun Univ. of Technol.
Volume
1
fYear
2006
fDate
4-6 Oct. 2006
Firstpage
28
Lastpage
30
Abstract
Aiming at the problem that the data in the information fusion often overloads, the method that rough set application in neural network was proposed, in which useful attributes were extracted from given training data and redundant attributes were deleted utilizing numerical analysis ability of rough set theory, so sample size can be reduced. While reducing training time and increasing efficiency, the useful information in the source data set wasn´t lost
Keywords
neural nets; numerical analysis; rough set theory; sensor fusion; multisensor information fusion; neural network; numerical analysis; rough set theory; training data; Data analysis; Data mining; Electronic mail; Information systems; Neural networks; Numerical analysis; Rough sets; Set theory; Systems engineering and theory; Training data; information fusion; neural network; rough set;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Engineering in Systems Applications, IMACS Multiconference on
Conference_Location
Beijing
Print_ISBN
7-302-13922-9
Electronic_ISBN
7-900718-14-1
Type
conf
DOI
10.1109/CESA.2006.4281618
Filename
4281618
Link To Document